Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add proffesor-for-testing/agentic-qe --skill worker-benchmarksgit clone --depth 1 https://github.com/proffesor-for-testing/agentic-qeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/proffesor-for-testing/agentic-qe/worker-benchmarks)<a href="https://agentmods.dev/skills/proffesor-for-testing/agentic-qe/worker-benchmarks"><img src="https://agentmods.dev/badge/skills/proffesor-for-testing/agentic-qe/worker-benchmarks/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/proffesor-for-testing/agentic-qe/worker-benchmarks"><img src="https://agentmods.dev/badge/skills/proffesor-for-testing/agentic-qe/worker-benchmarks.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00013 | $0.00956 |
| Opus 5.5 | $0.00005 | $0.00382 |
| Sonnet 5.5 | $0.00003 | $0.00191 |
| Haiku 4.5 | $0.00001 | $0.00096 |
Grade A, and why
worker-benchmarks scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to worker-benchmarks — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Worker Benchmarks Skill
Run comprehensive performance benchmarks for the agentic-flow worker system.
Quick Start
# Run full benchmark suite
npx agentic-flow workers benchmark
# Run specific benchmark
npx agentic-flow workers benchmark --type trigger-detection
npx agentic-flow workers benchmark --type registry
npx agentic-flow workers benchmark --type agent-selection
npx agentic-flow workers benchmark --type concurrent
Benchmark Types
1. Trigger Detection (trigger-detection)
Tests keyword detection speed across 12 worker triggers.
- Target: p95 < 5ms
- Iterations: 1000
- Metrics: latency, throughput, histogram
2. Worker Registry (registry)
Tests CRUD operations on worker entries.
- Target: p95 < 10ms
- Iterations: 500 creates, gets, updates
- Metrics: per-operation latency breakdown
3. Agent Selection (agent-selection)
Tests performance-based agent selection.
- Target: p95 < 1ms
- Iterations: 1000
- Metrics: selection confidence, agent scores
4. Model Cache (cache)
Tests model caching performance.
- Target: p95 < 0.5ms
- Metrics: hit rate, cache size, eviction stats
5. Concurrent Workers (concurrent)
Tests parallel worker creation and updates.
- Target: < 1000ms for 10 workers
- Metrics: per-worker latency, memory usage
6. Memory Key Generation (memory-keys)
Tests memory pattern key generation.
- Target: p95 < 0.1ms
- Iterations: 5000
- Metrics: unique patterns, throughput
Output Format
═══════════════════════════════════════════════════════════
📈 BENCHMARK RESULTS
═══════════════════════════════════════════════════════════
✅ Trigger Detection
Operation: detect
Count: 1,000
Avg: 0.045ms | p95: 0.120ms (target: 5ms)
Throughput: 22,222 ops$s
Memory Δ: 0.12MB
✅ Worker Registry
Operation: crud
Count: 1,500
Avg: 1.234ms | p95: 3.456ms (target: 10ms)
Throughput: 810 ops$s
Memory Δ: 2.34MB
───────────────────────────────────────────────────────────
📊 SUMMARY
───────────────────────────────────────────────────────────
Total Tests: 6
Passed: 6 | Failed: 0
Avg Latency: 0.567ms
Total Duration: 2345ms
Peak Memory: 8.90MB
═══════════════════════════════════════════════════════════
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 136 lines · 13 tokens per session scan A f1a8d511da98
worker-benchmarks is a skill published in the GitHub repository proffesor-for-testing/agentic-qe (506 stars, last pushed today), licensed MIT. It adds 13 tokens to every session and 956 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to worker-benchmarks, differing in 0 lines, and is treated as a copy.
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